Data exploration is not only about creating numbers and summary statistics. Sometimes a good plot can reveal more insights than a whole dataframe filled with numbers (especially to the human eye). In this exercise we make use of what we’ve just learned about plots with ggplot2. This time we are going to use all of the Gapminder GDP data.

1

Load the gapminder GDP data from the Excel file and convert it to long format (as in the Summary Statistics exercise). You can simply reuse the code from the exercises on Summary statistics for this, but make sure that you do not exclude data for the years 1970 to 2001.

Remember that we used the filter() function for choosing the individual time periods, so you need to exclude that line from the previous exercises here.

In the previous exercises we only analyzed how the period from 1960 to 1969 compares to the period from 2002 to 2011. The nice thing about plots is that we can make use of the whole range of years and still identify differences between various periods. Our plot of choice, therefore, is a line plot to visualize the data as a time series.

2

Plot the gapminder data as a line plot to display a time series.

Instead of geom_point as in the slides, the name of the geom we need is geom_line. In addition, in the aesthetics definition aes() you should define a grouping variable group = 1. Otherwise, ggplot assumes that you want to plot one line for each year.

Admittedly, this may not be the best approach to identify differences between the periods directly. We don’t know when our periods start and when they end. Luckily, this can be done in two relatively straightforward steps. Let’s start with the first one: using different colors for different time periods. For this purpose, we need an indicator variable as a grouping variable, so that we can use different colors for the line at each period.

3

Create an indicator variable for the time periods 1960-1969, 2002-2011 and the time inbetween.

A combination of mutate() and the if_else lets you create the new variables we need. To get some sensible legend labels later you should specify the indicator variables as strings.

After we’re set up with our indicator variable, it’s plotting time again. We can simply reuse our code from before and define a grouping color in the aesthetics definition.

4

Plot the line plot once again, but this time with different colors for the different time periods.

In the aesthetics defintion aes(), you can choose the option color = indicator_variable to define the grouping.

Now we can see some visual differences between the different periods. One last thing, however, is that there are way too many labels on the x-axis. Maybe a more sensible axis labeling approach would be to create axis breaks for every ten years steps. However, this is an advanced exercise as we did not talk about manipulating axes before. If you’re not feeling adventurous just jump to the next exercise which is also optional but probably more exciting.

5 (advanced)

Create some prettier, i.e., more sensible breaks for the x-axis.

You can modify the x-axis with scale_x_discrete() and its breaks with the option breaks = breaks_vector.

So far, we have only looked at bivariate relationships (in this case between time and global GDP). However, the true power of data visualization with ggplot2 lies in the exploration of multivariate relationships. Have a look at the following code:

library(gapminder)

gapminder %>% 
  filter(??? == ???) %>% 
  ggplot(aes(x = ???,
             y = ???,
             size = ???,
             color = ???)) +
  geom_???()

6 (optional)

Fill in the missing parts marked by ???. Say that we want to…

  1. plot data for the year 2007,

  2. visualize the relationship between GDP per capita and life expectancy,

  3. use population size to determine the size of the points in the plot,

  4. have different colors for different continents,

  5. and choose a proper to geom for plotting everything.

Think about in which kind of plot different sizes of geom-types actually make sense and are ‘on point’.